In addition to evaluating the merging algorithms on the standalone task of correction detection, we have also plugged in the merging algorithms into an end-to-end system in which every automatically detected correction is further classified into an error type.

Page 4, “Experimental Setup”

5 We currently do not evaluate the end-to-end system over different corpora.

Page 5, “Conclusions”

Table 4: Extrinsic evaluation, where we plugged the two merging models into an end-to-end feedback detection system by Swanson and Yamangil.

Maximum Entropy

Appears in 3 sentences as: Maximum Entropy (3)

In Improved Correction Detection in Revised ESL Sentences

To predict whether two basic-edits address the same writing problem more discriminatively, we train a Maximum Entropy binary classifier based on features extracted from relevant contexts for the basic edits.

Page 3, “A Classifier for Merging Basic-Edits”

MaXEntMerger We use the Maximum Entropy classifier to predict whether we should merge the two edits, as described in Section 34.

Page 4, “Experimental Setup”

We use a Maximum Entropy classifier along with features suggested by Swanson and Yamangil for this task.